Summary In the United States, more than 2 million cases of prescription drug-related adverse events (AE) occur annually, including 100,000 deaths. With more than 30% of the approved drugs implicated, drug-induced pulmonary disease (DIPD) is an underdiagnosed major safety concern. As many as 10% of patients who receive chemotherapeutic agents develop a lung- related AE. DIPD may present in a variety of clinical syndromes such as asthma, pulmonary fibrosis, pulmonary edema and interstitial pneumonia, and several of these manifestations are often fatal and irremediable. Currently, no alternatives exist for patients subjected to DIPD because the cause of DIPD is the primary medication and mitigating DIPD necessitates the immediate cessation of the suspected medication, which is either unlikely or not practical in most therapeutic regimens (e.g., bleomycin use in chemotherapy). On the other hand, treatment of complex multifactorial disorders may lead to inadvertent polypharmacy predisposing patients to drug-interaction induced pulmonary disease. Thus, understanding the molecular mechanisms underlying DIPD and finding safer and effective therapeutic regimens is of paramount importance. Building on our earlier and ongoing work in post-marketing surveillance of approved drugs, we will use the drug-related adverse events (AE) data from the United States Food and Drug Administration's Adverse Events Reporting Systems (FAERS) and the compound screening data from the NIH's Library of Integrated Network-based Cellular Signatures (LINCS) database as the basis for computational models that integrate network analyses with systems biology approaches to elucidate the mechanistic relationships of DIPD and guide the therapeutic interventions. Systematic deep mining of FAERS should allow us to find not only DIPD- predisposing drugs or drug combinations, but also beneficial approved drugs or drug combinations which can mitigate DIPD. Our two specific aims are to: (i) Find novel drug combinations that aggravate or mitigate DIPD and (ii) Delineate and characterize the DIPD- mitigating effects of drug combinatorials. Successful completion of this study can help generate testable hypotheses underlying DIPD mechanism, intervention, therapeutics choice, and drug repositioning for DIPD. Computational models generated from this study can help guide the design of in vitro assays, focused animal studies, and ultimately improved methods for DIPD prevention and intervention.